Freeze_support() - Error

[Ali_Cengiz]

Hi All,

I have installed the requirements for the code below, but I am getting an error. I’ve been trying for days but I couldn’t solve the error somehow. I would be very happy if you could help.

Code:

import torch.backends.cudnn as cudnn

from utils import google_utils
from utils.datasets import *
from utils.utils import *

import cv2
import numpy as np
import pytesseract
from multiprocessing import Pool, Manager
import functools
import imutils
import ctypes
from imutils import paths
import string

manager_plaka_reader = Manager()
camera_plaka_reader_val = manager_plaka_reader.Value(ctypes.Array, [])
counter_lock_plaka_reader = manager_plaka_reader.Lock()

manager_plaka_reader_text = Manager()
camera_plaka_reader_val_text = manager_plaka_reader_text.Value(ctypes.Array, [])
counter_lock_plaka_reader_text = manager_plaka_reader_text.Lock()

def build_tesseract_options(psm=7):
    # tell Tesseract to only OCR alphanumeric characters
    alphanumeric = "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
    options = "-c tessedit_char_whitelist={}".format(alphanumeric)
    # set the PSM mode
    options += " --psm {}".format(psm)
    # return the built options string
    return options

def detect(camera_plaka_reader_val, camera_plaka_reader_val_text):
    out, source, weights, view_img, save_txt, imgsz = "inference/output", "video1.mp4", "weights/best_licplate_small.pt", True, True, 640
    webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')

    # Initialize
    device = torch_utils.select_device("0")
    if os.path.exists(out):
        shutil.rmtree(out)  # delete output folder
    os.makedirs(out)  # make new output folder
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
    google_utils.attempt_download(weights)
    model = torch.load(weights, map_location=device)['model'].float().eval()  # load FP32 model
    imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
    if half:
        model.half()  # to FP16

    # Second-stage classifier
    classify = False
    if classify:
        modelc = torch_utils.load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
        modelc.to(device).eval()

    # Set Dataloader
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # Get names and colors
    names = model.module.names if hasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]

    # Run inference
    t0 = time.time()
    img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
    _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once

    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Inference
        t1 = torch_utils.time_synchronized()
        pred = model(img, augment=True)[0]

        # Apply NMS
        pred = non_max_suppression(pred, 0.35, 0.4, classes=0, agnostic=True)
        t2 = torch_utils.time_synchronized()

        # Apply Classifier
        if classify:
            pred = apply_classifier(pred, modelc, img, im0s)

        text = camera_plaka_reader_val_text.value[:]

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
            else:
                p, s, im0 = path, '', im0s

            save_path = str(Path(out) / Path(p).name)
            txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            
            if det is not None and len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += '%g %ss, ' % (n, names[int(c)])  # add to string
                
                # Write results
                for *xyxy, conf, cls in det:
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format

                    if view_img:  # Add bbox to image
                        label = '%s %.2f' % (names[int(cls)], conf)
                        c1,c2 = plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
                        try:
                            start_x = c1[0]
                            end_x = c2[0]
                            start_y = c1[1]
                            end_y = c2[1]  
                            im_crop = im0[start_y:end_y,start_x:end_x]
                            #cv2.imshow("crop",im_crop)
                            camera_plaka_reader_val.value = im_crop                   
                        except:
                            pass
                        
            # Print time (inference + NMS)
            #print('%sDone. (%.3fs)' % (s, t2 - t1))

            # Stream results
            if view_img:
                try:
                    if len(text) > 0:
                        cv2.putText(im0, text[:-2], (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
                except:
                    pass

                cv2.imshow(p, im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

    #print('Done. (%.3fs)' % (time.time() - t0))

def ProjectMain(camera_plaka_reader_val, camera_plaka_reader_val_text):
    lpText_List = []
    options = build_tesseract_options(psm=7)

    while True:
        try:
            img_plaka = camera_plaka_reader_val.value[:]
            gray = cv2.cvtColor(img_plaka, cv2.COLOR_BGR2GRAY)
            
            rectKern = cv2.getStructuringElement(cv2.MORPH_RECT, (13, 5))
            blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, rectKern)
            
            img_plaka = cv2.threshold(blackhat, 50, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
            # apply automatic license plate recognition
            
            lpText = pytesseract.image_to_string(img_plaka, config=options)
            
            if lpText != None and lpText not in lpText_List:
                lpText_List = []
                lpText_List.append(lpText)
            elif lpText != None and lpText in lpText_List:
                lpText_List.append(lpText)
            
            if lpText != None and len(lpText_List)>1 and len(lpText)>5: # This line for eliminate some wrong detect. You can replace "len(lpText_List)>1" to "len(lpText_List)>0" to see all results.
                camera_plaka_reader_val_text.value = lpText
                print("[INFO] {}".format(lpText))
            else:
                pass
                camera_plaka_reader_val_text.value = []
        except:
            continue

def smap(f):
    return f()

def main():
    f_ProjectMain = functools.partial(ProjectMain,camera_plaka_reader_val, camera_plaka_reader_val_text)
    f_plakaReader = functools.partial(detect,camera_plaka_reader_val, camera_plaka_reader_val_text)

    with Pool() as pool:
        res = pool.map(smap, [f_ProjectMain, f_plakaReader])

if __name__ == '__main__':
    with torch.no_grad():
        main()

Error Details:

  File "alicengiz.py", line 12
    from multiprocessing import Pool, Manager
       ^
IndentationError: expected an indented block

(yeniplaka) C:\plaka\yolov5>python alicengiz.py
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\spawn.py", line 105, in spawn_main
    exitcode = _main(fd)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\spawn.py", line 114, in _main
    prepare(preparation_data)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\spawn.py", line 225, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
    run_name="__mp_main__")
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\plaka\yolov5\alicengiz.py", line 19, in <module>
    manager_plaka_reader = Manager()
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\context.py", line 56, in Manager
    m.start()
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\managers.py", line 563, in start
    self._process.start()
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\process.py", line 112, in start
    self._popen = self._Popen(self)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\popen_spawn_win32.py", line 46, in __init__
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
    _check_not_importing_main()
  File "C:\Users\Test\anaconda3\envs\yeniplaka\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
    is not going to be frozen to produce an executable.''')
RuntimeError:
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

Thanks.

[ptrblck]

It seems you have a syntax error in:

  File "alicengiz.py", line 12
    from multiprocessing import Pool, Manager
       ^
IndentationError: expected an indented block

[emildepp]

@ptrblck

IndentationError: expected an indented block

This error message occurs when the Python interpreter encounters a code block that should be indented, but isn’t. Python indentation is a common issue in Python because whitespace is used to define code blocks, so proper indentation is critical for code to run correctly. Some common causes of this error include:

• Forgetting to indent the statements within a compound statement.
• Forgetting to indent the statements of a user-defined function.

To fix the error, make sure that all lines in the code block are properly indented by the same number of spaces or tabs. . It is recommended to use 4 spaces for indentation in Python, tabulation or a different number of spaces may work, but it is also known to cause trouble at times. Tabs are a bad idea because they may create different amount if spacing in different editors .